Project summary The use of chest auscultations to ?listen? to and diagnose lung infections has been in practice since the invention of the stethoscope in the early 1800s. While it is a versatile tool that is universally used to complement clinical observation and other diagnosis methods (e.g. chest palpation, X-rays), it remains an outdated technology that has not evolved much beyond its early design. Its use is limited by subjectivity and inconsistency in interpreting chest sounds, inter-listener variability, need for advanced medical expertise as well as vulnerability to ambient noise that masks the presence of sound patterns of interest. In the current project, we propose to design a novel smart stethoscope to automate diagnosis of chest auscultations; especially for pediatric use. Over 2 million children die every year of acute lower respiratory infections (ALRI), the leading cause of childhood mortality worldwide. Our hypothesis is that if lung sounds are robustly acquired and analyzed, they are sufficiently informative to result in quantifiable improvements in detection accuracy of lung pathologies. By improving diagnosis capability using a low-cost technology, the proposed smart stethoscope will enhance resource and case management of ALRI, especially in impoverished settings that lack alternative diagnosis tools such as X-rays. This proposal focuses on two key components for improving efficacy of lung auscultation diagnosis: Aim 1: Designing the smart stethoscope technology. This effort takes a different engineering direction than devices currently on the market by employing novel transducer and microphone arrays in a layout that mitigates issues with ambient noise and signal stability. The expected outcome is to provide medical practitioners with a low-cost device that offers noise-control, signal amplification and stable recordings. We will test this technology at the Johns Hopkins Pediatric Emergency Hospital. Aim 2: Augmenting the smart stethoscope with computer-aided diagnosis. We propose adaptive signal processing methods for analyzing lung signals to enable differentiating normal from pathological cases. The expected outcome is to improve the specificity and sensitivity of lung diagnosis using computer-aided analyses, and help inform clinical decisions and ALRI case management. The efficacy of the algorithm is directly evaluated in a study at a children's hospital in Peru, using radiographic pneumonia as benchmark. The site is chosen as representative of applicability of the proposed device in a low-resource setting. The proposal is a multidisciplinary effort that draws upon the expertise of engineers and medical experts, with close interaction and ongoing validation in patient populations. Its overarching goal is to improve sensitivity and specificity of pulmonary diagnosis using auscultations. The overall outcome of this effort is a point-of-care technology that is effective, low-cost, and deployable for pulmonary-health monitoring in hospitals, clinics, low resource community centers, and potentially home-based monitoring.